In massive Multiple-Input-Multiple-Output (MIMO) networks, base stations are typically equipped with relatively greater numbers of antenna elements than base stations in conventional MIMO networks. The greater number of antenna elements enables the energy of the beamformed transmissions to be focused into smaller regions of space, thereby providing enhanced throughput and radiated energy efficiency. This, in turn, allows for lower transmit power levels and reduced multiuser processing complexity.
Despite these advantages, massive MIMO brings about some new challenges not faced by conventional MIMO networks. One such challenge relates to a base station's acquisition of state information of the downlink channels between itself and its associated user equipment (UEs). In conventional MIMO networks, the UEs estimate the downlink channel coefficients based on training sequences sent by the base station and then send the estimated channel state information (CSI) to the base station. This approach, however, may result in significant overhead in massive MIMO networks because training sequence transmissions and channel state information feedback are proportional to the number of antennas at the base station. Another approach in MIMO networks configured for time division duplex (TDD) operation assumes there is channel reciprocity between uplink and downlink channels. This assumption allows the base station to estimate a downlink channel response based on uplink pilot signal transmissions from the UEs, thereby avoiding the large overhead of downlink training sequence transmission and explicit CSI feedback. However, channel reciprocity may not be a reliable assumption in massive MIMO systems for various reasons, such as hardware performance limitations and/or calibration errors in time division duplexed (TDD) massive MIMO uplink/downlink channel configurations, as well as frequency selective fading in frequency division duplexed (FDD) massive MIMO uplink/downlink channel configurations. As a result, some level of downlink training sequence transmission and CSI feedback may be required to support massive MIMO networks. Techniques for reducing overhead related to downlink training sequence transmission and CSI feedback in massive MIMO networks are therefore desired.